1. Field of the Invention
The present invention relates to a pattern matching apparatus and method that considers distance and direction, and more particularly, to a pattern matching apparatus and method for classifying and matching an input pattern that considers direction as well as distance between the input pattern and reference patterns.
2. Description of the Related Art
In general, pattern matching is a widely used method in a recognition system relating to pattern recognition, character recognition or sound recognition. In pattern matching, an unknown pattern is identified by investigating the degree of similarity and the degree of matching between the features of predetermined reference patterns and the unknown pattern. In order to recognize a large pattern set in real time, a high-speed pattern searching method is required. However, most actual pattern identifying methods trade-off the speed of recognition and the degree of accuracy. That is, recognition performance is low at high processing speeds, while processing speed is low in systems having excellent recognition performance.
Also, in conventional pattern matching, a predetermined number of the nearest/farthest neighbor reference patterns (hereinafter, referred to as model) of each pattern set includes unnecessary models, so that the space in memory is ineffectively occupied and the searching space is increased, thereby decreasing processing speed. Also, for patterns that are difficult to identify, only a fixed number of reference patterns are stored and used for matching, even though there are many similar patterns. This lowers the degree of accuracy.
In a conventional pattern identifying method, in which only the distance between two model vectors is considered a database having the nearest/farthest neighbor models is used. If the distance between an input pattern and a model is greater than a predetermined value .alpha., it is very likely that the model is far from the input pattern. Thus, k nearest neighbor patterns in the database of the model are excluded from the target to be compared for recognition (hereinafter referred to as recognition comparison target). In other words, even if a model to be compared with the input pattern is located near the boundary of a circle having radius .alpha. centered at the input pattern location, the predetermined number of the nearest neighbor models belonging to the model in the database are excluded from the recognition comparison target. However, it is very likely that the predetermined number of the nearest neighbor models adjacent to the model to be compared are excluded from the matching comparison target even though they are actually near the input pattern when the model to be compared is close to the boundary. Accordingly, the degree of accuracy in recognition is lowered.